Comparing Predictive Performance of Time Invariant and Time Variant Clinical Prediction Models in Cardiac Surgery.
Stud Health Technol Inform
; 310: 1026-1030, 2024 Jan 25.
Article
in En
| MEDLINE
| ID: mdl-38269970
ABSTRACT
Clinical prediction models are increasingly used across healthcare to support clinical decision making. Existing methods and models are time-invariant and thus ignore the changes in populations and healthcare practice that occur over time. We aimed to compare the performance of time-invariant with time-variant models in UK National Adult Cardiac Surgery Audit data from Manchester University NHS Foundation Trust between 2009 and 2019. Data from 2009-2011 were used for initial model fitting, and data from 2012-2019 for validation and updating. We fitted four models to the data a time-invariant logistic regression model (not updated), a logistic model which was updated every year and validated it in each subsequent year, a logistic regression model where the intercept is a function of calendar time (not updated), and a continually updating Bayesian logistic model which was updated with each new observation and continuously validated. We report predictive performance over the complete validation cohort and for each year in the validation data. Over the complete validation data, the Bayesian model had the best predictive performance.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Models, Statistical
/
Cardiac Surgical Procedures
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Adult
/
Humans
Language:
En
Journal:
Stud Health Technol Inform
Journal subject:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: